The platform features a fully modular video sentiment analysis framework consisting of data management, feature extraction, model training, and result analysis modules.
Object rearrangement is important for many applications but remains challenging, especially in confined spaces, such as shelves, where objects cannot be accessed from above and they block reachability to each other.
Specifically, supervised contrastive learning based on a memory bank is first used to train each new task so that the model can effectively learn the relation representation.
It is composed of two main modules: open intent detection and open intent discovery.
Joint entity and relation extraction is an essential task in information extraction, which aims to extract all relational triples from unstructured text.
Ranked #2 on Relation Extraction on SemEval-2010 Task 8
Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN.
DFSDP is extended to solve single-buffer, non-monotone instances, given a choice of an object and a buffer.
In this paper, we propose the Cross-Modal BERT (CM-BERT), which relies on the interaction of text and audio modality to fine-tune the pre-trained BERT model.
Ranked #1 on Multimodal Sentiment Analysis on MOSI